Recent advances in interstitial lung disease research

The interstitial lung diseases are a diverse collection of disorders characterized by impaired gas exchange, restricted physiology on lung function testing, and diffuse parenchymal lung infiltrates on radiography. Although the interstitial lung diseases are many, in routine clinical practice, the most commonly encountered in general internal medicine practice are sarcoidosis, idiopathic pulmonary fibrosis, and connective tissue disease-associated interstitial lung diseases. In immunocompromised patients, infection is the most common cause of diffuse lung infiltrates and must be ruled out before any attempt to treat with immune altering agents like corticosteroids. This review will focus on the more clinically significant recent advances in the broad field of interstitial lung disease research, with emphasis on the more common interstitial lung diseases occurring in immunocompetent hosts.peer-reviewe

Proceedings: Regenerative Medicine for Lung Diseases: A CIRM Workshop Report.

The mission of the California Institute of Regenerative Medicine (CIRM) is to accelerate treatments to patients with unmet medical needs. In September 2016, CIRM sponsored a workshop held at the University of California, Los Angeles, to discuss regenerative medicine approaches for treatment of lung diseases and to identify the challenges remaining for advancing such treatments to the clinic and market approval. Workshop participants discussed current preclinical and clinical approaches to regenerative medicine in the lung, as well as the biology of lung stem cells and the role of stem cells in the etiology of various lung diseases. The outcome of this effort was the recognition that whereas transient cell delivery approaches are leading the way in the clinic, recent advances in the understanding of lung stem cell biology, in vitro and in vivo disease modeling, gene editing and replacement methods, and cell engraftment approaches raise the prospect of developing cures for some lung diseases in the foreseeable future. In addition, advances in in vitro modeling using lung organoids and "lung on a chip" technology are setting the stage for high quality small molecule drug screening to develop treatments for lung diseases with complex biology. Stem Cells Translational Medicine 2017;6:1823-1828

Nanoparticles in the treatment of chronic lung diseases

Nanoparticles, although considered a topic of modern medicine, actually have an interesting history. Currently, advances in nanomedicine hold great promise as drug carrier systems for sustained release and targeted delivery of diverse therapeutic agents. Nanoparticles can be defined as complex drug carrier systems which incorporate and protect a certain drug or particle. Nanoparticles can be administered via different routes, such as intravenous injection, oral administration, or pulmonary inhalation. Even though the use of nano-carriers via pulmonary inhalation is heavily debated, this system represents an attractive alternative to the intravenous or oral routes, due to the unique anatomical and physiological features of the lungs and the minimal interactions between the targeted site and other organs. Some of the widely used nano-carriers for the treatment of chronic pulmonary diseases, via pulmonary route, are as follows: polymeric nanoparticles, liposomal nano-carriers, solid lipid nanoparticles, and submicron emulsions. Nano-carrier systems provide the advantage of sustained-drug release in the lung tissue resulting in reduced dosing frequency and improved patient compliance. Further studies focusing on understanding the mechanisms of action of nanoparticles and improving their chemical structure are required in order to better understand the potential long-term risk of excipient toxicity and nanoscale carriers

Interstitial Lung Diseases

Interstitial lung diseases are rare and diffuse, and their diagnosis is a challenge because it requires a multidisciplinary approach. Future trends in the treatment of these diseases requires knowledge of the molecular changes in various types of lung cells involved in disease occurence and development. This book presents readers with a better understanding of the etiology, development, and treatment of interstitial lung diseases

The role of nailfold capillaroscopy in interstitial lung diseases - Can it differentiate idiopathic cases from collagen tissue disease associated interstitial lung diseases?

Introduction: Nailfold capillaroscopy (NFC) is a non-invasive diagnostic test that is mostly used for early diagnosis of collagen tissue diseases (CTDs). We aimed to evaluate whether NFC findings could be a clue for discriminating idiopathic interstitial lung diseases (ILD) from CTD associated ILDs (CTD-ILD). Additionally it was aimed to determine whether NFC could be helpful in discriminating usual interstitial pneumonia (UIP) pattern from non-specific interstitial pneumonia (NSIP) pattern. Materials and Methods: We grouped patients into three main groups: 15 CTD-ILD, 18 idiopathic ILD, and 17 patients in the control group. The CTD-ILD group was split into two subgroups: 8 patients with Sjögren’s syndrome (SJS)-associated ILD and 7 with rheumatoid arthritis (RA)-associated ILD. The idiopathic-ILD group consisted of 10 idiopathic NSIP and 8 IPF patients. The control group consisted of 10 SJS and 7 RA patients without lung disease. None of the patients were on acute exacerbation at the time of examination, and none had Reynaud’s phenomenon. Results: Mean capillary density was significantly reduced only in the CTD-ILD group as compared to the control group (p= 0.006). In subgroup analysis, it was determined that RA-ILD, IPF, and SJSILD subgroups had more severe capillaroscopic abnormalities. Mean capillary density in patients with the UIP pattern was reduced compared to patients with the NSIP pattern and those in the control group; p values were 0.008 and < 0.001, respectively. Conclusion: This study is to be the first describing and comparing the nailfold capillaroscopic findings of patients with NSIP and UIP patterns. NFC findings can be helpful in discriminating UIP patterns from NSIP patterns. But to show its role in differentiating idiopathic disease, more studies with more patients are needed. © 2015, Ankara University. All rights reserved

Deep Learning Paradigms for Existing and Imminent Lung Diseases Detection: A Review

Diagnosis of lung diseases like asthma, chronic obstructive pulmonary disease, tuberculosis, cancer, etc., by clinicians rely on images taken through various means like X-ray and MRI. Deep Learning (DL) paradigm has magnified growth in the medical image field in current years. With the advancement of DL, lung diseases in medical images can be efficiently identified and classified. For example, DL can detect lung cancer with an accuracy of 99.49% in supervised models and 95.3% in unsupervised models. The deep learning models can extract unattended features that can be effortlessly combined into the DL network architecture for better medical image examination of one or two lung diseases. In this review article, effective techniques are reviewed under the elementary DL models, viz. supervised, semi-supervised, and unsupervised Learning to represent the growth of DL in lung disease detection with lesser human intervention. Recent techniques are added to understand the paradigm shift and future research prospects. All three techniques used Computed Tomography (C.T.) images datasets till 2019, but after the pandemic period, chest radiographs (X-rays) datasets are more commonly used. X-rays help in the economically early detection of lung diseases that will save lives by providing early treatment. Each DL model focuses on identifying a few features of lung diseases. Researchers can explore the DL to automate the detection of more lung diseases through a standard system using datasets of X-ray images. Unsupervised DL has been extended from detection to prediction of lung diseases, which is a critical milestone to seek out the odds of lung sickness before it happens. Researchers can work on more prediction models identifying the severity stages of multiple lung diseases to reduce mortality rates and the associated cost. The review article aims to help researchers explore Deep Learning systems that can efficiently identify and predict lung diseases at enhanced accuracy